MOFU: Development of a MOrphing Fluffy Unit with Expansion and Contraction Capabilities and Evaluation of the Animacy of Its Movements
Robots designed for therapy and social interaction aim to evoke a sense of animacy in humans. While many studies have focused on life like appearance or joint based movements, the effect of whole body volume changing movements commonly observed in living organisms has received little attention. In this study, we developed MOFU MOrphing Fluffy Unit, a mobile robot capable of whole body expansion and contraction using a single motor enclosed in a fluffy exterior. MOFU employs a Jitterbug geometric transformation mechanism that enables smooth diameter changes from approximately 210 mm to 280 mm with a single actuator, and is equipped with a differential two wheel drive mechanism for locomotion. We conducted an online survey using videos of MOFU behaviors and evaluated perceived animacy using the Godspeed Questionnaire Series. First, we compared stationary conditions with and without expansion contraction and with and without rotational motion. Both expansion contraction and rotation independently increased perceived animacy. Second, we examined whether presenting two MOFUs simultaneously would further enhance animacy perception, but no significant difference was observed. Exploratory analyses were also conducted across four dual robot motion conditions. Third, when expansion contraction was combined with locomotion, animacy ratings were higher than for locomotion alone. These results suggest that whole body volume changing movements enhance perceived animacy in robots, indicating that physical volume change is an important design element for future social and therapeutic robots.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Morphing Wing Designs in Commercial Aviation
With increasing demands for fuel efficiency and operational adaptability in commercial aviation}, this paper provides a systematic review and classification of morphing wing technologies, analyzing their aerodynamic perf…
MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis
Conventional methods for human motion synthesis are either deterministic or struggle with the trade-off between motion diversity and motion quality. In response to these limitations, we introduce MoFusion, i.e., a new de…
DenoisingDiversityMotion SynthesisStableMoFusion: Towards Robust and Efficient Diffusion-based Motion Generation Framework
Thanks to the powerful generative capacity of diffusion models, recent years have witnessed rapid progress in human motion generation. Existing diffusion-based methods employ disparate network architectures and training …
DenoisingMotion GenerationDemoFusion: Democratising High-Resolution Image Generation With No $$$
High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but, due to the enormous capital investment required for training, it is increasingly centralised to a few large corp…
Image GenerationPrivacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors
The main question this work aims at answering is: "can morphing attack detection (MAD) solutions be successfully developed based on synthetic data?". Towards that, this work introduces the first synthetic-based MAD devel…